Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging
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Date
2023-01-31
Publication Type
Journal Article
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yes
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Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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published
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Volume
2 (1)
Pages / Article No.
Publisher
PLOS
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Software
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09614 - Vayena, Eftychia / Vayena, Eftychia
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Funding
187356 - Digital Health Innovation: a Governance Roadmap for Switzerland (D-GOVmap) (SNF)